Merge pull request #1012 from Mv77/ContribMod/RiskyAssetClass

Class for risky-asset consumers

Documentation/CHANGELOG.md

@@ -17,6 +17,7 @@ Release Data: TBD
 #### Minor Changes
 
 * Fix bug in DCEGM's primary kink finder due to numpy no longer accepting NaN in integer arrays [#990](https://github.com/econ-ark/HARK/pull/990).
+* Add a general class for consumers who can save using a risky asset [#1012](https://github.com/econ-ark/HARK/pull/1012/).
 
 ### 0.11.0
 

HARK/ConsumptionSaving/ConsPortfolioModel.py

@@ -17,6 +17,8 @@
     init_idiosyncratic_shocks  # Baseline dictionary to build on
 )
 
+from HARK.ConsumptionSaving.ConsRiskyAssetModel import RiskyAssetConsumerType
+
 from HARK.distribution import combine_indep_dstns
 from HARK.distribution import Lognormal, Bernoulli  # Random draws for simulating agents
 from HARK.interpolation import (
@@ -118,7 +120,7 @@ def __init__(
         self.dvdsFuncFxd = dvdsFuncFxd
 
 
-class PortfolioConsumerType(IndShockConsumerType):
+class PortfolioConsumerType(RiskyAssetConsumerType):
     """
     A consumer type with a portfolio choice. This agent type has log-normal return
     factors. Their problem is defined by a coefficient of relative risk aversion,
@@ -130,7 +132,7 @@ class PortfolioConsumerType(IndShockConsumerType):
     of the risky asset's return distribution must also be specified.
     """
 
-    time_inv_ = deepcopy(IndShockConsumerType.time_inv_)
+    time_inv_ = deepcopy(RiskyAssetConsumerType.time_inv_)
     time_inv_ = time_inv_ + ["AdjustPrb", "DiscreteShareBool"]
 
     def __init__(self, cycles=1, verbose=False, quiet=False, **kwds):
@@ -139,12 +141,10 @@ def __init__(self, cycles=1, verbose=False, quiet=False, **kwds):
         kwds = params
 
         # Initialize a basic consumer type
-        IndShockConsumerType.__init__(
+        RiskyAssetConsumerType.__init__(
             self, cycles=cycles, verbose=verbose, quiet=quiet, **kwds
         )
 
-        shock_vars = ['PermShk', 'TranShk','Adjust','Risky']
-
         # Set the solver for the portfolio model, and update various constructed attributes
         self.solve_one_period = solveConsPortfolio
         self.update()
@@ -154,9 +154,8 @@ def pre_solve(self):
         self.update_solution_terminal()
 
     def update(self):
-        IndShockConsumerType.update(self)
-        self.update_RiskyDstn()
-        self.update_ShockDstn()
+
+        RiskyAssetConsumerType.update(self)
         self.update_ShareGrid()
         self.update_ShareLimit()
 
@@ -206,86 +205,6 @@ def update_solution_terminal(self):
             dvdsFuncFxd=dvdsFuncFxd_terminal,
         )
 
-    def update_RiskyDstn(self):
-        """
-        Creates the attributes RiskyDstn from the primitive attributes RiskyAvg,
-        RiskyStd, and RiskyCount, approximating the (perceived) distribution of
-        returns in each period of the cycle.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
-        """
-        # Determine whether this instance has time-varying risk perceptions
-        if (
-            (type(self.RiskyAvg) is list)
-            and (type(self.RiskyStd) is list)
-            and (len(self.RiskyAvg) == len(self.RiskyStd))
-            and (len(self.RiskyAvg) == self.T_cycle)
-        ):
-            self.add_to_time_vary("RiskyAvg", "RiskyStd")
-        elif (type(self.RiskyStd) is list) or (type(self.RiskyAvg) is list):
-            raise AttributeError(
-                "If RiskyAvg is time-varying, then RiskyStd must be as well, and they must both have length of T_cycle!"
-            )
-        else:
-            self.add_to_time_inv("RiskyAvg", "RiskyStd")
-
-        # Generate a discrete approximation to the risky return distribution if the
-        # agent has age-varying beliefs about the risky asset
-        if "RiskyAvg" in self.time_vary:
-            self.RiskyDstn = []
-            for t in range(self.T_cycle):
-                self.RiskyDstn.append(
-                    Lognormal.from_mean_std(
-                        self.RiskyAvg[t],
-                        self.RiskyStd[t]
-                    ).approx(self.RiskyCount)
-                )
-            self.add_to_time_vary("RiskyDstn")
-
-        # Generate a discrete approximation to the risky return distribution if the
-        # agent does *not* have age-varying beliefs about the risky asset (base case)
-        else:
-            self.RiskyDstn = Lognormal.from_mean_std(
-                self.RiskyAvg,
-                self.RiskyStd,
-            ).approx(self.RiskyCount)
-            self.add_to_time_inv("RiskyDstn")
-
-    def update_ShockDstn(self):
-        """
-        Combine the income shock distribution (over PermShk and TranShk) with the
-        risky return distribution (RiskyDstn) to make a new attribute called ShockDstn.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
-        """
-        if "RiskyDstn" in self.time_vary:
-            self.ShockDstn = [
-                combine_indep_dstns(self.IncShkDstn[t], self.RiskyDstn[t])
-                for t in range(self.T_cycle)
-            ]
-        else:
-            self.ShockDstn = [
-                combine_indep_dstns(self.IncShkDstn[t], self.RiskyDstn)
-                for t in range(self.T_cycle)
-            ]
-        self.add_to_time_vary("ShockDstn")
-
-        # Mark whether the risky returns and income shocks are independent (they are)
-        self.IndepDstnBool = True
-        self.add_to_time_inv("IndepDstnBool")
-
     def update_ShareGrid(self):
         """
         Creates the attribute ShareGrid as an evenly spaced grid on [0.,1.], using
@@ -337,53 +256,6 @@ def update_ShareLimit(self):
             self.ShareLimit = SharePF
             self.add_to_time_inv("ShareLimit")
 
-    def get_Risky(self):
-        """
-        Sets the shock RiskyNow as a single draw from a lognormal distribution.
-        Uses the attributes RiskyAvgTrue and RiskyStdTrue if RiskyAvg is time-varying,
-        else just uses the single values from RiskyAvg and RiskyStd.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
-        """
-        if "RiskyDstn" in self.time_vary:
-            RiskyAvg = self.RiskyAvgTrue
-            RiskyStd = self.RiskyStdTrue
-        else:
-            RiskyAvg = self.RiskyAvg
-            RiskyStd = self.RiskyStd
-        RiskyAvgSqrd = RiskyAvg ** 2
-        RiskyVar = RiskyStd ** 2
-
-        mu = np.log(RiskyAvg / (np.sqrt(1.0 + RiskyVar / RiskyAvgSqrd)))
-        sigma = np.sqrt(np.log(1.0 + RiskyVar / RiskyAvgSqrd))
-        self.shocks['Risky'] = Lognormal(
-            mu, sigma, seed=self.RNG.randint(0, 2 ** 31 - 1)
-        ).draw(1)
-
-    def get_Adjust(self):
-        """
-        Sets the attribute AdjustNow as a boolean array of size AgentCount, indicating
-        whether each agent is able to adjust their risky portfolio share this period.
-        Uses the attribute AdjustPrb to draw from a Bernoulli distribution.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
-        """
-        self.shocks['Adjust'] = Bernoulli(
-            self.AdjustPrb, seed=self.RNG.randint(0, 2 ** 31 - 1)
-        ).draw(self.AgentCount)
-
     def get_Rfree(self):
         """
         Calculates realized return factor for each agent, using the attributes Rfree,
@@ -422,8 +294,7 @@ def initialize_sim(self):
         # these need to be set because "post states",
         # but are a control variable and shock, respectively
         self.controls["Share"] = np.zeros(self.AgentCount)
-        self.shocks['Adjust'] = np.zeros(self.AgentCount, dtype=bool)
-        IndShockConsumerType.initialize_sim(self)
+        RiskyAssetConsumerType.initialize_sim(self)
 
     def sim_birth(self, which_agents):
         """
@@ -445,24 +316,6 @@ def sim_birth(self, which_agents):
         # here a shock is being used as a 'post state'
         self.shocks['Adjust'][which_agents] = False
 
-    def get_shocks(self):
-        """
-        Draw idiosyncratic income shocks, just as for IndShockConsumerType, then draw
-        a single common value for the risky asset return.  Also draws whether each
-        agent is able to update their risky asset share this period.
-
-        Parameters
-        ----------
-        None
-
-        Returns
-        -------
-        None
-        """
-        IndShockConsumerType.get_shocks(self)
-        self.get_Risky()
-        self.get_Adjust()
-
     def get_controls(self):
         """
         Calculates consumption cNrmNow and risky portfolio share ShareNow using